{
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    "colab": {
      "name": "Assignment-2.ipynb",
      "provenance": [],
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    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e6ss8wKQkivW",
        "colab_type": "text"
      },
      "source": [
        "# **Assignment** - **2** "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LNa1xkz4ej4t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Importing all the required modules\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.linear_model import Lasso"
      ],
      "execution_count": 376,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E2S0iNSXjWsy",
        "colab_type": "text"
      },
      "source": [
        "# **Data** **Splitting**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kb69h4OAj64V",
        "colab_type": "text"
      },
      "source": [
        "**Task** - **1**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yz2SUi_ofPRj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Task 1\n",
        "def split_data(data,ratio):\n",
        "  np.random.seed(42)\n",
        "  #Shuffling the data so as to randomnly choose the train and test set\n",
        "  shuffle = np.random.permutation(len(data))\n",
        "  set_size = int(len(data)*ratio)\n",
        "  #Dividing the given data into Test and Train dataset\n",
        "  test_index = shuffle[:set_size]\n",
        "  train_index = shuffle[set_size:]\n",
        "  return data.iloc[train_index],data.iloc[test_index]"
      ],
      "execution_count": 377,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VrgjrdZbwMie",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "f79f6762-8d0b-4d2a-ace0-dfa5571c3eec"
      },
      "source": [
        "#Reading the given dataset \n",
        "data = pd.read_csv(\"Advertising Dataset.csv\")\n",
        "test_data,train_data = split_data(data,0.75)\n",
        "#Printing the test_data and train_data shape to check if they are allotted properly\n",
        "print('Shape of Test Data:',test_data.shape)\n",
        "print('Shape of Training data:',train_data.shape)\n",
        "#Printing the head of the data so as check features available\n",
        "print('The Data:')\n",
        "data.head()"
      ],
      "execution_count": 378,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of Test Data: (50, 5)\n",
            "Shape of Training data: (150, 5)\n",
            "The Data:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>TV</th>\n",
              "      <th>radio</th>\n",
              "      <th>newspaper</th>\n",
              "      <th>sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>230.1</td>\n",
              "      <td>37.8</td>\n",
              "      <td>69.2</td>\n",
              "      <td>22.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>44.5</td>\n",
              "      <td>39.3</td>\n",
              "      <td>45.1</td>\n",
              "      <td>10.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>17.2</td>\n",
              "      <td>45.9</td>\n",
              "      <td>69.3</td>\n",
              "      <td>9.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>151.5</td>\n",
              "      <td>41.3</td>\n",
              "      <td>58.5</td>\n",
              "      <td>18.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>180.8</td>\n",
              "      <td>10.8</td>\n",
              "      <td>58.4</td>\n",
              "      <td>12.9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0     TV  radio  newspaper  sales\n",
              "0           1  230.1   37.8       69.2   22.1\n",
              "1           2   44.5   39.3       45.1   10.4\n",
              "2           3   17.2   45.9       69.3    9.3\n",
              "3           4  151.5   41.3       58.5   18.5\n",
              "4           5  180.8   10.8       58.4   12.9"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 378
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gPDNNH42F2ih",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "d72592ec-fcfe-4b1e-db9f-5d481432281f"
      },
      "source": [
        "#Getting some details about the training set\n",
        "print('The Training Data')\n",
        "print(train_data.shape)\n",
        "#Printing the head of the data so as check features available\n",
        "train_data.head()"
      ],
      "execution_count": 379,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Training Data\n",
            "(150, 5)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>TV</th>\n",
              "      <th>radio</th>\n",
              "      <th>newspaper</th>\n",
              "      <th>sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>96</td>\n",
              "      <td>163.3</td>\n",
              "      <td>31.6</td>\n",
              "      <td>52.9</td>\n",
              "      <td>16.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>16</td>\n",
              "      <td>195.4</td>\n",
              "      <td>47.7</td>\n",
              "      <td>52.9</td>\n",
              "      <td>22.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>31</td>\n",
              "      <td>292.9</td>\n",
              "      <td>28.3</td>\n",
              "      <td>43.2</td>\n",
              "      <td>21.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>158</th>\n",
              "      <td>159</td>\n",
              "      <td>11.7</td>\n",
              "      <td>36.9</td>\n",
              "      <td>45.2</td>\n",
              "      <td>7.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>129</td>\n",
              "      <td>220.3</td>\n",
              "      <td>49.0</td>\n",
              "      <td>3.2</td>\n",
              "      <td>24.7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Unnamed: 0     TV  radio  newspaper  sales\n",
              "95           96  163.3   31.6       52.9   16.9\n",
              "15           16  195.4   47.7       52.9   22.4\n",
              "30           31  292.9   28.3       43.2   21.4\n",
              "158         159   11.7   36.9       45.2    7.3\n",
              "128         129  220.3   49.0        3.2   24.7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 379
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sCVw7RpdF3Ax",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "1909af07-405e-4b9a-fb5f-85e82d78254f"
      },
      "source": [
        "#Getting some details about the testing set\n",
        "print('The Test Data')\n",
        "print(test_data.shape)\n",
        "#Printing the head of the data so as check features available\n",
        "test_data.head()"
      ],
      "execution_count": 380,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Test Data\n",
            "(50, 5)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>TV</th>\n",
              "      <th>radio</th>\n",
              "      <th>newspaper</th>\n",
              "      <th>sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>177</td>\n",
              "      <td>248.4</td>\n",
              "      <td>30.2</td>\n",
              "      <td>20.3</td>\n",
              "      <td>20.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>181</th>\n",
              "      <td>182</td>\n",
              "      <td>218.5</td>\n",
              "      <td>5.4</td>\n",
              "      <td>27.4</td>\n",
              "      <td>12.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>90</td>\n",
              "      <td>109.8</td>\n",
              "      <td>47.8</td>\n",
              "      <td>51.4</td>\n",
              "      <td>16.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>8.6</td>\n",
              "      <td>2.1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>14</td>\n",
              "      <td>97.5</td>\n",
              "      <td>7.6</td>\n",
              "      <td>7.2</td>\n",
              "      <td>9.7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Unnamed: 0     TV  radio  newspaper  sales\n",
              "176         177  248.4   30.2       20.3   20.2\n",
              "181         182  218.5    5.4       27.4   12.2\n",
              "89           90  109.8   47.8       51.4   16.7\n",
              "8             9    8.6    2.1        1.0    4.8\n",
              "13           14   97.5    7.6        7.2    9.7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 380
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0eFAwWgxwNAT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "456d01b2-4a5e-4ec1-e866-7fa387ae7b84"
      },
      "source": [
        "#Creating the training data , by allocating the features to the sales\n",
        "X_train,y_train = np.array([train_data['TV'].values,train_data['radio'].values,train_data['newspaper'].values]),(train_data['sales'].values).reshape(1,150)\n",
        "print(X_train.shape,y_train.shape)"
      ],
      "execution_count": 381,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(3, 150) (1, 150)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Rw4kejhjkXE",
        "colab_type": "text"
      },
      "source": [
        "# **Multiple** **Linear** **Regression** **Model**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "viY1ZeRaj90Y",
        "colab_type": "text"
      },
      "source": [
        "**Task** - **2**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PyspdyikwM1U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "847b92b7-d412-42ac-ec42-5e466ef6e1cd"
      },
      "source": [
        "#Task2\n",
        "#Calculating the mean of tv,radio,newspaper and sales\n",
        "x_mean = np.mean(X_train,axis=1,keepdims=True)\n",
        "y_mean = np.mean(y_train)\n",
        "print('X-mean:',x_mean)\n",
        "print('Y-mean:',y_mean)"
      ],
      "execution_count": 382,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X-mean: [[144.71466667]\n",
            " [ 23.416     ]\n",
            " [ 30.06533333]]\n",
            "Y-mean: 13.981333333333332\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NnMhqwgYx5ML",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Calculating the required so as to find w-matrix\n",
        "covariance = np.dot((X_train-x_mean),(y_train-y_mean).T)\n",
        "var = np.dot((X_train-x_mean),(X_train-x_mean).T)\n",
        "#Taking inverse of variance matrix\n",
        "var = np.linalg.inv(var)"
      ],
      "execution_count": 383,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fXRwpKiDyAu8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "b5cbccd4-8e2b-4e48-ce47-62abbcc1eb21"
      },
      "source": [
        "#Calculating the coeff-matrix\n",
        "w=np.dot(covariance.T,var)\n",
        "#Calculating the bias for the model\n",
        "b=y_mean-np.dot(w,x_mean)\n",
        "print('Co-efficients:',w)\n",
        "print('Bias:',b)\n"
      ],
      "execution_count": 384,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Co-efficients: [[ 0.04575751  0.18843966 -0.00578168]]\n",
            "Bias: [[3.12087614]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aR8J8QRoyA_o",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "6e839872-f27b-4e93-ddfd-2d38fb3ce26a"
      },
      "source": [
        "#Using the Python Model To predict the co-efficients and intercept\n",
        "reg = LinearRegression().fit(X_train.T, y_train.T)\n",
        "reg.score(X_train.T, y_train.T)\n",
        "#Printing those values\n",
        "print('Co-efficients:',reg.coef_)\n",
        "print('Bias:',reg.intercept_)"
      ],
      "execution_count": 385,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Co-efficients: [[ 0.04575751  0.18843966 -0.00578168]]\n",
            "Bias: [3.12087614]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KapWaxGJ7LC7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "cf9c3464-21b2-4dbd-98ab-1ad7c611649b"
      },
      "source": [
        "#Comparing co-efficients by plotting in a Bar Plot\n",
        "new_df = [{'TV': w[0][0],'Radio':w[0][1] ,'Newspaper':w[0][2]}, \n",
        "          {'TV': reg.coef_[0][0],'Radio':reg.coef_[0][1] ,'Newspaper':reg.coef_[0][2]}]\n",
        "df = pd.DataFrame(new_df,index =['Model', 'Python Model'])\n",
        "model = ['Model','Python Model']\n",
        "df['model'] = model\n",
        "#Plottinf the bar plots , thus comparing them using seaborn\n",
        "fig, axs = plt.subplots(figsize=(10,4),ncols=3)\n",
        "sns.barplot(data= df,x=\"model\", y=\"TV\", palette=\"Oranges\" ,ax=axs[0])\n",
        "sns.barplot(data= df,x=\"model\", y=\"Radio\", palette=\"Greens\",ax=axs[1])\n",
        "sns.barplot(data= df,x=\"model\", y=\"Newspaper\", palette=\"Purples\",ax=axs[2])\n",
        "plt.tight_layout()"
      ],
      "execution_count": 386,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x288 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WK1jw8hbkAZ3",
        "colab_type": "text"
      },
      "source": [
        "**Task** - **3**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uWfJQfwN7CVD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Task-3\n",
        "#Allocating our attributes and sales to corresponding test data\n",
        "X_test , y_test = np.array([test_data['TV'].values,test_data['radio'].values,test_data['newspaper'].values]) , test_data['sales'].values"
      ],
      "execution_count": 387,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nVdFWYyIPfHD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "093d45d3-bb0d-4015-8d0f-a436d17ad05d"
      },
      "source": [
        "#Predicting the the X_test values  \n",
        "y_pred= b + np.dot(w,X_test)\n",
        "\n",
        "#Calculating and Printing the Mean Square error\n",
        "mse=np.sum((y_pred-y_test)**2)\n",
        "n=X_test.shape[1]\n",
        "mse = (mse/n)\n",
        "print('Mean Sqaured Error:',mse)\n"
      ],
      "execution_count": 388,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Sqaured Error: 3.4531805967396734\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9zcccE9zjuDW",
        "colab_type": "text"
      },
      "source": [
        "# **Ridge** **Regression**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AwPpcO3DkDHb",
        "colab_type": "text"
      },
      "source": [
        "**Task** - **4**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-aUsepUn6ul5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Task-4\n",
        "#Performing Ridge regression , thus alloting the required variables\n",
        "lmbda=[0.5, 1, 5, 10, 50, 100]\n",
        "I=np.identity(X_train.shape[0])\n",
        "coefs_mean =[]\n",
        "coefs_max=[]\n",
        "coefs=[]\n",
        "mse=[]\n",
        "b_rr=[]"
      ],
      "execution_count": 389,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3XM-sQcbU8za",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Going into a loop to calculate \n",
        "for l in lmbda:\n",
        "  #Finding the w-matrix for the ridge regression,for different lambdas\n",
        "  cov_ridge = np.dot((X_train-x_mean),(y_train-y_mean).T)\n",
        "  var_ridge = np.linalg.inv(np.dot((X_train-x_mean),(X_train-x_mean).T)+l*I)\n",
        "  w_ridge=(np.dot(var_ridge,cov_ridge)).T\n",
        "  #Finding the intercept for different lambdas\n",
        "  b_ridge=y_mean-np.dot((w_ridge),x_mean)\n",
        "  b_rr.append(b_ridge.tolist())\n",
        "\n",
        "  coefs.append(w_ridge[0].tolist())\n",
        "  coefs_max.append(np.max(w_ridge[0]).tolist())\n",
        "  coefs_mean.append(np.mean(w_ridge[0]).tolist())\n",
        "  #Finding the mean squared error for different lambdas\n",
        "  \n",
        "  y_pred= b_ridge + np.dot(w_ridge,X_test)\n",
        "  mse_ridge=np.sum((y_pred-y_test)**2)\n",
        "  n=X_test.shape[1]\n",
        "  mse_ridge = (mse_ridge/n)\n",
        "  mse.append(mse_ridge)"
      ],
      "execution_count": 390,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OEDFZLVAZrAb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "outputId": "9a916482-5a9b-42ad-9d32-d9bb70f9ef89"
      },
      "source": [
        "#Printing the above obtained results\n",
        "for i in range(6):\n",
        "  print('Lambda =',lmbda[i])\n",
        "  print('Co-efficients :',coefs[i])\n",
        "  print('Intercept :',b_rr[i])\n",
        "  print('The mean squared error :',mse[i])"
      ],
      "execution_count": 391,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Lambda = 0.5\n",
            "Co-efficients : [0.045757506915486454, 0.18843632919543382, -0.005780894973542443]\n",
            "Intercept : [[3.120930422465282]]\n",
            "The mean squared error : 3.453181682380154\n",
            "Lambda = 1\n",
            "Co-efficients : [0.04575750751400622, 0.18843300307552752, -0.005780112925447722]\n",
            "Intercept : [[3.120984707737769]]\n",
            "The mean squared error : 3.4531827724979944\n",
            "Lambda = 5\n",
            "Co-efficients : [0.04575751227875822, 0.18840639854055324, -0.005773857894611344]\n",
            "Intercept : [[3.1214189304121227]]\n",
            "The mean squared error : 3.4531916545671315\n",
            "Lambda = 10\n",
            "Co-efficients : [0.04575751817620287, 0.1883731539285864, -0.005766042489570217]\n",
            "Intercept : [[3.121961560041507]]\n",
            "The mean squared error : 3.453203159691977\n",
            "Lambda = 50\n",
            "Co-efficients : [0.04575756302003052, 0.18810763849152612, -0.005703654321960516]\n",
            "Intercept : [[3.1262966589008983]]\n",
            "The mean squared error : 3.4533112451056627\n",
            "Lambda = 100\n",
            "Co-efficients : [0.0457576132552168, 0.18777684392386837, -0.005626005502837095]\n",
            "Intercept : [[3.131700737099049]]\n",
            "The mean squared error : 3.453486187559663\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P75RF8qfdBPP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "052ed23c-88d1-48be-b490-15aa09057b04"
      },
      "source": [
        "#Plotting the co-efficients against each other thereby comparting them\n",
        "plt.figure(figsize=(7,7))\n",
        "plt.title('Plotting the coefficients against each other')\n",
        "\n",
        "#Plotting Co-eff mean v/s lambda\n",
        "plt.subplot(2, 1, 1) \n",
        "plt.plot(lmbda,coefs_mean,'ro-')\n",
        "plt.xlabel('Lambda',fontsize=10)\n",
        "plt.ylabel('Co-eff Mean',fontsize=10)\n",
        "plt.grid(True)\n",
        "\n",
        "#Plotting Co-eff max v/s lambda\n",
        "plt.subplot(2, 1, 2)\n",
        "plt.plot(lmbda,coefs_max,'bo-')\n",
        "plt.xlabel('Lambda',fontsize=10)\n",
        "plt.ylabel('Co-eff Max',fontsize=10)\n",
        "plt.grid(True) \n",
        "\n",
        "plt.tight_layout()"
      ],
      "execution_count": 392,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-xO7ejsD6vUY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "outputId": "b8c003f5-4b22-4a45-a33d-ab634d91971e"
      },
      "source": [
        "#Comparing the Mean Squared Error for different Lambda's\n",
        "plt.figure(figsize=(5,5))\n",
        "plt.plot(lmbda,mse,'o-')\n",
        "plt.ylabel('Mean Squared Error',fontsize=10)\n",
        "plt.xlabel('Lambda',fontsize=10)\n",
        "plt.grid(True) "
      ],
      "execution_count": 393,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8ReTupoCj0Tg",
        "colab_type": "text"
      },
      "source": [
        "# **Lasso** **Regression**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Eg37QgqckGht",
        "colab_type": "text"
      },
      "source": [
        "**Task** - **5**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aZCvo7Jec5T_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "outputId": "7b1f5853-7538-4c6c-e1f1-16a7db84c7ae"
      },
      "source": [
        "#Task-5\n",
        "#Lasso Regression\n",
        "lasso = Lasso()\n",
        "coeff=[]\n",
        "alphas = [0.5, 1, 5, 10, 50, 100]\n",
        "most_imp=''\n",
        "#Finding the coeff for different alphas \n",
        "for a in alphas:\n",
        "    lasso.set_params(alpha=a)\n",
        "    lasso.fit(X_train.T, y_train.T)\n",
        "    coeff.append(lasso.coef_)\n",
        "    print(lasso.coef_)\n",
        "    if(((lasso.coef_[0]==0) and (lasso.coef_[1]==0)) or((lasso.coef_[2]==0) and (lasso.coef_[1]==0)) or ((lasso.coef_[2]==0)and (lasso.coef_[1]==0))):\n",
        "      if lasso.coef_[2]==0 and lasso.coef_[1]==0 :\n",
        "        most_imp='TV'\n",
        "      if lasso.coef_[2]==0 and lasso.coef_[0]==0 :\n",
        "         most_imp='Radio'\n",
        "      if lasso.coef_[0]==0 and lasso.coef_[1]==0:\n",
        "         most_imp='Newspaper'\n",
        "#Plotting Co-efficients vs Lambda\n",
        "plt.figure(figsize=(7,5))   \n",
        "ax = plt.gca()\n",
        "ax.plot(lmbda, coeff)\n",
        "ax.set_xscale('log')\n",
        "plt.axis('tight')\n",
        "plt.legend(('TV','Radio','Newspaper'))\n",
        "plt.xlabel('Lambda')\n",
        "plt.ylabel('Co-efficients')"
      ],
      "execution_count": 394,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 0.04569456  0.18523656 -0.00406609]\n",
            "[ 0.04563146  0.18203603 -0.00235105]\n",
            "[0.04524952 0.16237428 0.        ]\n",
            "[0.04480362 0.13933604 0.        ]\n",
            "[0.04077729 0.         0.        ]\n",
            "[0.03396629 0.         0.        ]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Co-efficients')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 394
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ee+45Fi5cyAUXXEB+fn6FPpW3cXeef/75srpWrlxZdh3U0ntA7imNGEWSjRn0PxF6joSZNwXnHZdMg+MfCCbqSL0Tz8gukdq0acOpp57KlClTOO+88wA45phjeOCBB7jmmmsAmD9/PoMHD6Zr16588803FBQUsM8++3DYYYcxadIkHnzwQQCWL1/OPvvsw+WXX87KlStZsGAB++yzDytXrmT27NkMHz6cp59+msMOO6wsBNu1a8eWLVt47rnnGD9+fFldzzzzDKNHj+bdd9+lZcuWtGzZkjFjxvDAAw/wwAMPYGZ8+OGHtX73Do0YRZJVkzZw0kNw5vPBPSgfGwsvXR1c51Wklv3iF7/gm2++KXt9//33k5uby6BBg+jXrx8PP/xw2bqDDjqI/fbbDwjuv7h69WoOO+wwAJ599lkGDBjA4MGDWbRoEWeddRYA+++/P5MnT6Zv3758++23XHLJJbRq1YoLLriAAQMGMGbMGIYOHVqhpuzsbIYMGcLFF1/MlClTALj55pspLCxk0KBB9O/fn5tvvrnW/xa67ZRIKti+Bd64Dd57BFp0huPug95HR12V7IGGdNupFStW8IMf/IBFixbFvc2oUaOYNGkSOTlV3hlqt+zubac0YhRJBVnNYNydcP4rkNkU/joe/n4hbF0fdWUi9Y6CUSSVdB0GF8+CkdfCoudh8rDgsQEc+ZHU1aNHj90aLQK89dZbtTJarAkFo0iqyciCI26CC9+GVl3hufPgbxNg0+qoKxOpFxSMIqmq4wA4/zU45nZY/hb84WDIfSy4ZZakhIYwxyNqNfkbKxhFUll6BhzyM7j0P9Dpe/CvK+GJ42D9Z1FXJtXIzs5m/fr1CscEcnfWr19Pdnb2bm2nWaki9YU7fPAkvHIzFG+H0TfCwT8NwlOSTmFhIatWrarwg3apfdnZ2XTp0oVGjRpVaN/VrFQFo0h9s3kNvPQL+Pgl6DQYTngwuD+kiJSJ7OcaZjbWzD42s2Vmdn0V60ea2QdmVmRm4yutKzaz+eEyLaa9p5m9F+7zGTPLTORnEEk5LTrBaX+FUx6HzavDy8rdpsvKicQpYcFoZunAZGAc0A+YYGb9KnVbCZwDPF3FLra5++BwOT6m/U7gXnfvBXwLnF/rxYukOjPofxL8dC4MPBVmTYJHRsDKOVFXJpL0EjliHAYsc/fl7l4ATAVOiO3g7ivcfQEQ1zQ6C64oewTwXNj0BHBi7ZUsUs/EXlaucBv8eSxMv0aXlRPZhUQGY2fgy5jXq8K2eGWbWa6ZzTGz0vBrC2x096Ia7lOkYep1FFw6Bw66COb+Ef4wHD59LeqqRJJSMv9co3t4YvR04D4z23d3NjazC8NgzV23bl1iKhRJJaWXlTtvJjRqDH/9Ifz9IsjbEHVlIkklkcG4Guga87pL2BYXd18dPi4H3gKGAOuBVmZWOv98p/t090fdPcfdc9q3b7/71YvUV90OgovfDS8r9xw8OFSXlROJkchgfB/oHc4izQROA6ZVsw0AZtbazLLC5+2AQ4ElHvy25E2gdAbr2cA/a71ykfquqsvKTT09uDmySAOXsGAMzwNeBswElgLPuvtiM5toZscDmNlQM1sFnAI8YmaLw837Arlm9hFBEP7W3ZeE664DrjKzZQTnHKck6jOI1Hull5U7+jb47A2YfJAuKycNnn7gLyKB9Z/Bi1fAilnQYwQc93tou1un9kVShu7HKCLVa7svnP1iEIhrPoKHDoF/3w/FRdVvK1KPKBhFpJwZHHgO/PQ92PcIePVm+NOR8PXCqCsTqTMKRhHZUYu94bSnYfxjsGlVcFm5N26Hou1RVyaScApGEamaGQw4GS57HwaeAu/cDQ8fBivfi7oykYRSMIrIrjVpAyc9DGeUXlZuDEy/FrZviboykYRQMIpIfHofBZfOhmEXwNxH4Q8HwzJdVk7qHwWjiMQvqzkcezec93JwWbm//BBeuFiXlZN6RcEoIruv28Fw0SwYcTUs/D+YPAwW/yPqqkRqhYJRRGqmUTYceTNc+Ba06Az/dzaseDfqqkT2mIJRRPZMx4HBodWW3YJJOboggKQ4BaOI7LlGjWHsb2DtYsjV5YsltSkYRaR29PlBcLWcN+6ALboHqqQuBaOI1A4zGHcXFObB67dGXY1IjSkYRaT2tOsNwy+FD/8Cq3RHG0lNCkYRqV0jr4FmHWH61bqvo6QkBaOI1K6s5nDM7fDVh/DhU1FXI7LbFIwiUvsGjoduh8Brt+qqOJJyFIwiUvvMgkvH5W+EN38TdTUiu0XBKCKJ0XEADL0g+F3jmgVRVyMSNwWjiCTO6BuhcRuYcS24R12NSFwUjCKSOI1bwVG3wMrZwcXGRVKAglFEEmvwmbD3AfDKLyF/c9TViFRLwSgiiZWWBt+fBFvWwjt3RV2NSLUSGoxmNtbMPjazZWZ2fRXrR5rZB2ZWZGbjY9oHm9lsM1tsZgvM7Ecx6x43s8/NbH64DE7kZxCRWtD5QDjgxzDnIVj3cdTViOxSwoLRzNKBycA4oB8wwcz6Veq2EjgHeLpSex5wlrv3B8YC95lZq5j117j74HCZn5APICK168hbILOpJuJI0kvkiHEYsMzdl7t7ATAVOCG2g7uvcPcFQEml9k/c/dPw+VfAWqB9AmsVkURr2g6OuBmWvwVLp0VdjchOJTIYOwNfxrxeFbbtFjMbBmQCn8U03xEeYr3XzLL2rEwRqTMHngt7DYCZN0FBXtTViFQpqSffmFkn4CngXHcvHVXeAPQBhgJtgOt2su2FZpZrZrnr1unecCJJIT0juCLOpi/h3XujrkakSokMxtVA15jXXcK2uJhZC+Al4CZ3n1Pa7u5rPLAdeIzgkO0O3P1Rd89x95z27XUUViRpdD8EBp4K//49bFgedTUiO0hkML4P9DaznmaWCZwGxHViIez/AvCkuz9XaV2n8NGAE4FFtVq1iCTe0RMhvRG8fGPUlYjsIGHB6O5FwGXATGAp8Ky7LzaziWZ2PICZDTWzVcApwCNmtjjc/FRgJHBOFT/L+KuZLQQWAu2A2xP1GUQkQVp0gsOvg09mwCczo65GpALzBjBtOicnx3NzdTdxkaRSVAAPHwolRXDpHMjQPDqpO2Y2z91zqlqX1JNvRKQey8iEcXcG5xlnPxh1NSJlFIwiEp19j4C+x8E7k2DTqqirEQEUjCIStTG/AS8JLjIukgQUjCISrVbdYMQvYPELsPztqKsRUTCKSBI45HJo3SO4jmpxYdTVSAOnYBSR6DXKhrG/hXX/hbmPRl2NNHAKRhFJDvuNhV5Hw1u/he/+F3U10oApGEUkOZgFP98oyofXbo26GmnAFIwikjza7gvDL4OPnoaV70VdjTRQCkYRSS4jr4YWnWH61VBSHHU10gApGEUkuWQ2hWNuh68XwLzHo65GGqC4gtHMmppZWvh8PzM73swaJbY0EWmw+p8EPUbAG7dB3oaoq5EGJt4R4ztAtpl1Bl4Bfgw8nqiiRKSBM4Nxd0H+5iAcRepQvMFo7p4HnAz8wd1PAfonriwRafD26gcHXQS5j8FX86OuRhqQuIPRzIYDZwAvhW3piSlJRCQ06npo2g6mXwMlJVFXIw1EvMF4BXAD8EJ4s+F9gDcTV5aICJDdEo6eCKvmwoKpUVcjDUS8wbiXux/v7ncCuPtyYFbiyhIRCQ06DboMg1d/Bfmboq5GGoB4g/GGONtERGpXWhocezds/Sa4XJxIgmXsaqWZjQOOBTqb2f0xq1oARYksTESkzN6D4cBz4L1H4ICzoEPfqCuSeqy6EeNXQC6QD8yLWaYBYxJbmohIjCN/Bdktgok47lFXI/XYLkeM7v4R8JGZPe3uukmaiESnSRs44mZ46argpsYDTo66Iqmn4j3HOMzMXjWzT8xsuZl9bmbLE1qZiEhlB54DHQfBK7+E7VuirkbqqXiDcQpwD3AYMBTICR9FROpOWjocOwk2r4ZZv4u6Gqmn4g3GTe4+w93Xuvv60iWhlYmIVKXbQfC9CfCfB2D9Z1FXI/VQvMH4ppndbWbDzeyA0qW6jcxsrJl9bGbLzOz6KtaPNLMPzKzIzMZXWne2mX0aLmfHtB9oZgvDfd5vZhbnZxCR+uKoX0NGNsy4ThNxpNbtcvJNjIPCx5yYNgeO2NkGZpYOTAaOBlYB75vZNHdfEtNtJXAOcHWlbdsAt4Tv58C8cNtvgYeAC4D3gOnAWGBGnJ9DROqD5nvB6Btg5o3wycuw/7ioK5J6JK5gdPfRNdj3MGBZeJUczGwqcAJQFozuviJcV/kiiGOAV919Q7j+VWCsmb0FtHD3OWH7k8CJKBhFGp5hF8IHTwajxn1GQ6PsqCuSeiLe+zHuZWZTzGxG+LqfmZ1fzWadgS9jXq8K2+Kxs207h89rsk8RqU/SGwW3ptr4Bfzn/ur7i8Qp3nOMjwMzgb3D158AVyaioNpiZheaWa6Z5a5bty7qckQkEfY5PLip8azfwbdfRF2N1BPxBmM7d38WKAFw9yKguJptVgNdY153CdvisbNtV4fPq92nuz/q7jnuntO+ffs431ZEUs4xt4OlwSs3RV2J1BPxBuNWM2tLMBEGMzsYqO4y9+8Dvc2sp5llAqcRXEouHjOBY8ystZm1Bo4BZrr7GmCzmR0czkY9C/hnnPsUkfqoZRcY8QtY+iJ89kbU1Ug9EG8wXkUQavua2b+BJ4Gf7WqDcFR5GUHILQWeDe/lONHMjgcws6Fmtgo4BXjEzBaH224AbiMI1/eBiaUTcYBLgT8By4DP0MQbETnkZ9Bmn2AiTlFB1NVIijOP8zdAZpYB7A8Y8HEqXTs1JyfHc3Nzoy5DRBLpk1fg6VPg6Nvg0MujrkaSnJnNc/ecqtbtcsRoZkeEjycDxxME437AcWGbiEhy2O8Y2G8cvH0nbF4TdTWSwqo7lHp4+HhcFcsPEliXiMjuG/sbKC6EV38VdSWSwqq77dQt4eO5dVOOiMgeaLNPcBj1nbsh51zofkjUFUkKivcH/r8xs1Yxr1ub2e2JK0tEpIYOuwpadg1uaFxcFHU1koLinZU6zt03lr4Ir1l6bGJKEhHZA5lNYMwd8L9FMO+xqKuRFBRvMKabWVbpCzNrDGTtor+ISHT6Hg/7jII3boOt30RdjaSYeIPxr8DrZnZ+eI3UV4EnEleWiMgeMAuuo1qwFV7/ddTVSIqJKxjd/U7gDqBvuNzm7nclsjARkT3Sfn846GL44ClYPS/qaiSFxDtixN1nuPvV4TIzkUWJiNSKw6+DZh2CiTglle9uJ1K16n7g/274+J2ZbY5ZvjOzzXVToohIDWW3CK6Es3oezP9r1NVIiqhuxHgWgLs3d/cWMUtzd29RB/WJiOyZQadCt+Hw2q2w7duoq5EUUF0w/h+Amb1eB7WIiNS+0ok42zbAm/8v6mokBezyyjdAmpndCOxvZldVXunu9ySmLBGRWtRpEOScD+//EQ44CzoOiLoiSWLVjRhPI7ghcTrQvIpFRCQ1jL4RslsFE3HivKuQNEzVjRjHuvudZpbl7hPrpCIRkURo0gaOugVevAIWPQ8Dx0ddkSSp6kaMpRcPPzHRhYiIJNyQH8PeQ+CVX8L276KuRpJUdcG41Mw+JTjHuCBmWWhmC+qiQBGRWpOWDsdOgu/WBHfgEKlCdbedmmBmHYGZBDcqFhFJbV1yYMiZMPsPMPhMaL9f1BVJkqn2yjfu/rW7fw9YC2S7+xelS+LLExFJgCNvhUZNYMa1mogjO4j3fozHAfOBl8PXg81sWiILExFJmGbtg1mqy9+E//4r6mokycR7rdRbgWHARgB3nw/0TFBNIiKJN/Qn0KEfvHwjFG6LuhpJIvEGY6G7b6rUpuMPIpK60jPg2Lth00p4976oq5EkEm8wLjaz0wluWNzbzB4A/pPAukREEq/HYTBgPLx7L2z4POpqJEnEG4w/A/oD24GngU3AldVtZGZjzexjM1tmZtdXsT7LzJ4J179nZj3C9jPMbH7MUmJmg8N1b4X7LF3XIc7PICKyo2Nug7QMmHlT1JVIkoj3RsV57n6Tuw8Fjnf3X7p7/q62MbN0YDIwDugHTDCzfpW6nQ986+69gHuBO8P3+6u7D3b3wcCPgc/D85qlzihd71EMds8AABlYSURBVO5r4/kMIiJVarE3HH4tfPwSfPpq1NVIEoj7RsUxXoqz3zBgmbsvd/cCYCpwQqU+JwBPhM+fA440M6vUZ0K4rYhIYhx8KbTtBTOug6LtUVcjEatJMFYOrp3pDHwZ83pV2FZlH3cvIjhE27ZSnx8Bf6vU9lh4GPXmKoJURGT3ZGTCuDthw2cw5w9RVyMRq0kw/rHWq9gJMzsIyHP3RTHNZ7j7QGBEuPx4J9teaGa5Zpa7bt26OqhWRFJar6Ogzw/g7bth0+qoq5EIxR2MZraXmf0AWBnnhJfVQNeY113Ctir7mFkG0BJYH7P+NCqNFt19dfj4HcFEoGFVvbm7P+ruOe6e0759+zjKFZEGb8wd4MXw6s1RVyIRivfKN6cCc4FTgFOB98ysunu2vA/0NrOeZpZJEHKVr5YzDTg7fD4eeMM9uD6TmaWF71V2ftHMMsysXfi8EfADYBEiIrWhdQ847OfBbak+nxV1NRKReEeMNwFD3f1sdz+LYJS2y39ShecMLyO4APlS4Fl3X2xmE82s9ILkU4C2ZrYMuAqI/UnHSOBLd18e05YFzAzv7DGfYMRZZ4d2RaQBOPQKaNUtuI5qcWHU1UgEzOO4gK6ZLQzP65W+TgM+im1LZjk5OZ6bmxt1GSKSKpb+C545A8b+Fg6+JOpqJAHMbJ6751S1Lt4R48tmNtPMzjGzcwh+sjG9tgoUEUkqfb4P+x4Jb/4Gtuin0g3NLoPRzHqZ2aHufg3wCDAoXGYDj9ZBfSIidc8s+PlG4TZ47ddRVyN1rLoR433AZgB3/7u7X+XuVwEvhOtEROqndr1h+E9h/l/gy/ejrkbqUHXBuJe7L6zcGLb1SEhFIiLJYuQ10LwTTL8aSoqjrkbqSHXB2GoX6xrXZiEiIkknqxkcczusmQ8fPBl1NVJHqgvGXDO7oHKjmf0EmJeYkkREksiAH0L3w+D1iZC3IepqpA5UF4xXAueGt3r6Xbi8TXBXjCsSX56ISMRKJ+Lkb4I374i6GqkDGbta6e7/Aw4xs9HAgLD5JXd/I+GViYgki44DYNgFMPdROOAs6PS9qCuSBIr3foxvuvsD4aJQFJGGZ9QN0LgNTL8G4rgwiqSumtxdQ0Sk4WncCo7+NXz5Hix4JupqJIEUjCIi8fre6dA5B165GfI3R12NJIiCUUQkXmlpcOxdsHUdvH1n1NVIgigYRUR2R+cDgwk47z0Ma/8bdTWSAApGEZHddeQtkNksuDWVJuLUOwpGEZHd1bQtHPFL+PxtWPLPqKuRWqZgFBGpiZzzoONAmHkTFGyNuhqpRQpGEZGaSEuHcXfD5lUw656oq5FapGAUEamp7sNh0I/gP/fD+s+irkZqiYJRRGRPHD0R0jNh5o1RVyK1RMEoIrInmneEUdfDJy/Dxy9HXY3UAgWjiMieOuhiaLc/vHw9FOZHXY3sIQWjiMieSm8U3Jrq289h9gNRVyN7SMEoIlIb9h0NfY+Hd34HG7+MuhrZAwpGEZHaMia8kfErN0Vbh+yRhAajmY01s4/NbJmZXV/F+iwzeyZc/56Z9Qjbe5jZNjObHy4Px2xzoJktDLe538wskZ9BRCRurbrBiF8EV8NZ/lbU1UgNJSwYzSwdmAyMA/oBE8ysX6Vu5wPfunsv4F4g9nL1n7n74HC5OKb9IeACoHe4jE3UZxAR2W2H/Axa94Dp10JxYdTVSA0kcsQ4DFjm7svdvQCYCpxQqc8JwBPh8+eAI3c1AjSzTkALd5/j7g48CZxY+6WLiNRQo2wYeyd88zG890jU1UgNJDIYOwOxZ6BXhW1V9nH3ImAT0DZc19PMPjSzt81sREz/VdXsU0QkWvuPhd5j4K3fwndfR12N7KZknXyzBujm7kOAq4CnzazF7uzAzC40s1wzy123bl1CihQR2amx/w+Kt8Ort0RdieymRAbjaqBrzOsuYVuVfcwsA2gJrHf37e6+HsDd5wGfAfuF/btUs0/C7R519xx3z2nfvn0tfBwRkd3Qdt/gfOOCqbByTtTVyG5IZDC+D/Q2s55mlgmcBkyr1GcacHb4fDzwhru7mbUPJ+9gZvsQTLJZ7u5rgM1mdnB4LvIsQDdDE5HkNOIX0KIzTL8aSoqjrkbilLBgDM8ZXgbMBJYCz7r7YjObaGbHh92mAG3NbBnBIdPSn3SMBBaY2XyCSTkXu/uGcN2lwJ+AZQQjyRmJ+gwiInsks2nw28avF8K8x6KuRuJkweTO+i0nJ8dzc3NrvH1xibO1oAiA2CmzpRNorex1+Bi2VJ5fG/u6cp/yfVS9z9h1IpJC3OHJ42HNAvjZB9C0bfXbSMKZ2Tx3z6lqXUZdF5OKlq3dwpj73om6jB3EG6qGEf4Ps+B1mgX9LdzAwu1j2y1caUbQHj4v7WtWvr8d2qm4n7I+sW0GaWU1xPYN261i7WlVvCcV+lbe/47vn2axf4vy9eV1VPw8se+5q7/FjnXE7r9Se/j3p8r6gv7paUZ66WOakRa+zih9nha8X0ZaWtnz0n4ZYd+0tPLtS/eVZkZGupX1D/pBRloaaWlUeM/S/ukx+5MaMAtuaPzwofDGRDju91FXJNVQMMahffMsfvn9vhXaSgfajld6vev1FfcR37ZV9SltiOf9SjxsD/6Hu+Ne3l62rQdbl4TrvWw/pf0rtpduW7qdV9p/+fry5yUx+yvtWxLznJh9l5TE7INg5F5S9l7xvGcV7eHnq/C3CPdT+vmoqr2qv0Xp+4fPqdxexeev/J6pxIzywK0Q0kGANko3mmSm0ywrgyaZGTTNyqBpVnrwmFn6WN4e9Emv0FbaJ72+hXCHPjDsIpjzBzjgbOh8QNQVyS7oUKpIhMqDu/wfCSUe/AOguCT4x0FRSQnFHjwvdqe42IPHkvJ+ZYs7JZVel/eD4pKS4HEX/YqKY/Yb9isqCfuH+6mqX2FRCXmFxWzdXkTe9mK2bC8ir6CILduLySsoIq8g/skn2Y3SygKzLGyzMmhWGqilQRuGbrAuo0IwB9sEr7My0qI/FZG/CR7ICS4bd/6rkJasv5ZrGHQoVSRJlR5mDV9FWUrCFZc428LgDJZithaEzwvK2/NKnxeEfcLnm7cVsmbjNvIKgtDdur2IopL4/mGfnhaMZptmloflDqPbsrCtNLqt1F4azBnpuxls2S3h6Inwj4vho7/BkDNq8FeUuqBgFJE6kZ5mNAtHdrVle1FxzOi0fJRaMXjLwzVvezFbCorIC9ev2ZRfIZh3Z1SblZFWHpiVR7eZ4eg2ZiTbNCuDpo1Gcli7A2gy82aWtRpBkxZty0a32Y2SYFQrgIJRRFJYVkY6WRnptG6aWSv7Kylx8gqLydtetEPYbtlesb1C2IZt3+UX8b/N+RVGw4XFFUe1/W08L2bexOwp1zCx6Kyy9jSjbERbPmJNr+K8bKVRbNn6yudyazCqFUDBKCJSJi1mVNuhlvZZUFRS8dBwwSH8b9Yizln2DF2PvIivs/cpOw+7ZXulUW1BMV9vzi8P6LAtXpkZaeUj1syKh4nbNMukb6cW9N+7BX07tqBxZnotfeLUp8k3IiJ1LW8DPHAAdOgP5/xrxx8970JJ7LnamHOzFQ8bx56nrTiqLW37elM+m/OD32enGezTvhn9924RLi3pv3cLWjWpnZF4MtLkGxGRZNKkDRz5K/jXz2HR8zBwfNybpqVZ2aHTPeHurN64jcVfbWbxV5tZ8tUm5n6+gX/O/6qsT+dWjelXKSw7tcyu9+dCNWIUEYlCSTH8cTRsWQeXvQ9ZzaKuCID1W7azZM3mssBc/NUmPv9ma9nvbls3aVQWkv3CwOzZrmnK/fZ0VyNGBaOISFS+nAtTjobDfg5H3Rp1NTu1dXsR//06DMrVm1m8ZhOffL2FguISABo3Sqdvp+Zlgdl/75bs17EZWRnJe95SwahgFJFk9Y9LYcGzcOkcaNcr6mriVlBUwrK1W1j81abwUOxmlqzZzJbtwXnLjDSjV4dmMWEZjDCbZzeKuPKAglHBKCLJastaeOBA6DIUznx+tybiJJuSEmflhryyQ7Clh2O/2bK9rE/3tk3KRpWl5y87NM+u81o1+UZEJFk16wCjboCZN8DH06HP96OuqMbS0owe7ZrSo11Tvj+oU1n72s35FcJy0erNTF/4ddn69s2zdpgR261Nk8gm+WjEKCISteJCeHgEFG6Fn86FRo2jrijhNucXsiRmgs+Srzbz6dotFIeX+WuelUHfSmHZq0MzGtXSRQt0KFXBKCLJ7vN34InjYNSNMOq6qKuJRH5hMZ/877sKo8v/rvmObYXBRQ0yM9LYf6/m/HT0vowd0Kmave2aDqWKiCS7niOh/8nw7j3wvdOgdfeoK6pz2Y3SGdSlFYO6tCprKy5xPv9mS8zvLTeTkeA7k2jEKCKSLDathgdzYN8j4LS/Rl1NvbarEaOuMCsikixadoaRV8N//wXLXou6mgZLwSgikkyGXwZt9oEZ10FRQdTVNEgKRhGRZJKRBePugvXLYM4foq6mQVIwiogkm95Hw/7Hwjt3w+avqu8vtUrBKCKSjMb8Jvh946u/irqSBkfBKCKSjNr0hMOuhIX/Byv+HXU1DUpCg9HMxprZx2a2zMyur2J9lpk9E65/z8x6hO1Hm9k8M1sYPh4Rs81b4T7nh0tt3WhbRCS5HHoltOwK06+B4qKoq2kwEhaMZpYOTAbGAf2ACWbWr1K384Fv3b0XcC9wZ9j+DXCcuw8EzgaeqrTdGe4+OFzWJuoziIhEKrNJcEh17WLInRJ1NQ1GIkeMw4Bl7r7c3QuAqcAJlfqcADwRPn8OONLMzN0/dPfSM86LgcZmlpXAWkVEklPf42Cf0fDGHcFNjSXhEhmMnYEvY16vCtuq7OPuRcAmoG2lPj8EPnD37TFtj4WHUW+2qC6/LiJSF8yCn28UboXXfx11NQ1CUk++MbP+BIdXL4ppPiM8xDoiXH68k20vNLNcM8tdt07/yhKRFNZ+Pzj4UvjwKVg1L+pq6r1EBuNqoGvM6y5hW5V9zCwDaAmsD193AV4AznL3z0o3cPfV4eN3wNMEh2x34O6PunuOu+e0b9++Vj6QiEhkDr8WmnWE6b+AkpKoq6nXEhmM7wO9zaynmWUCpwHTKvWZRjC5BmA88Ia7u5m1Al4Crnf3snnKZpZhZu3C542AHwCLEvgZRESSQ1ZzOOY2+OrDYOQoCZOwYAzPGV4GzASWAs+6+2Izm2hmx4fdpgBtzWwZcBVQ+pOOy4BewK8q/SwjC5hpZguA+QQjzj8m6jOIiCSVgadAt+HBucZt30ZdTb2l206JiKSSrxfCIyNh6E/g2LujriZl6bZTIiL1RceBQSi+/6cgJKXWKRhFRFLN6BuhcevgijgN4KhfXVMwioikmsat4ahbYeXs4FqqUqsUjCIiqWjwmbD3AfDKLyF/c9TV1CsKRhGRVJSWBsdOgi3/g3fuirqaekXBKCKSqrocCEN+DHMegnWfRF1NvaFgFBFJZUfdCplNYYYm4tQWBaOISCpr2g5G/xKWvwVLX4y6mnpBwSgikupyzoMO/WHmjVCQF3U1KU/BKCKS6tIzgqvgbPoS3r036mpSnoJRRKQ+6HFocC3Vf/8eNnwedTUpTcEoIlJfHH0bpDcKDqlKjSkYRUTqixadgvs2fjwdPnkl6mpSloJRRKQ+OegSaNsbXr4OirZHXU1KUjCKiNQnGZkw7k7YsBxmPxh1NSlJwSgiUt/0OhL6/ADemQSbVkVdTcpRMIqI1EdjfgNeElxkXHaLglFEpD5q3R0OuwoWvwCfvxN1NSlFwSgiUl8dejm06g7Tr4XiwqirSRkKRhGR+qpRYxj7W1i3FOb+MepqUoaCUUSkPtt/HPQ6Ct76f/Dd/6KuJiUoGEVE6jMzGHsnFG6D126NupqUoGAUEanv2vWCQy6Dj56Gle9FXU3SS2gwmtlYM/vYzJaZ2fVVrM8ys2fC9e+ZWY+YdTeE7R+b2Zh49ykiIlUYcTU03zu4oXFJcdTVJLWEBaOZpQOTgXFAP2CCmfWr1O184Ft37wXcC9wZbtsPOA3oD4wF/mBm6XHuU0REKstqBmNuhzUfwQdPRF1NUkvkiHEYsMzdl7t7ATAVOKFSnxOA0v+HngOONDML26e6+3Z3/xxYFu4vnn2KiEhV+p8MPUbA6xMhb0PU1SStjATuuzPwZczrVcBBO+vj7kVmtgloG7bPqbRt5/B5dfsUiVyJl1BYUsj24u0UFBdQWBw+LwmeF5QUVFgX+9rdoy5f6rP+R8GGj+DvE6D9/lFXUyND9h3Hfr3GJWz/iQzGSJnZhcCFAN26dYu4GqkLxSXFFJQUBGETE0qVX+9qXWlwVdm3ZMeA29m6opKiqP8cIjvXthUUfwVffxV1JTVygxenbDCuBrrGvO4StlXVZ5WZZQAtgfXVbFvdPgFw90eBRwFycnL0T/AEKSopqjJASkNnh9dVBMrO+lU10ip9XraupHzUVeS1E0YZlkGj9EZkpmeSmZYZPFZ63jSjKZlZVa/b6fPY15XbY9rSTJPFpQ7kfQuempNwmjbpkND9JzIY3wd6m1lPgvA6DTi9Up9pwNnAbGA88Ia7u5lNA542s3uAvYHewFzA4thng+PuFJQUsLVwK3mFeeQV5QWP4fNtRdt2HUJxrqtq1FXiJbXyGTLSMqoNlGaZzWiT3qZmwRPPNuHz9LT0WvlMIkmtcbuoK0haCQvG8JzhZcBMIB34s7svNrOJQK67TwOmAE+Z2TJgA0HQEfZ7FlgCFAE/dQ/+aVPVPhP1GRKlsKSwQnDlFeaxtSgIta2FW9lWtK1CW15R2F64rex55fArrsG//BqlBaOirPSssueVQ6Nlo5ZVh81uBk9WelYwCtvJdholiUiysIZwoj8nJ8dzc3NrvH1RSREb8jdUGUg7jNJKg660bxX9C0viv5hv44zGNMloQtNGTWnSqAlNMppUeGzaqOkObZX7Z2dkVwy/9EwapTVSGIlIg2Vm89w9p6p19XbyTW36bONnjH9xfLX9stKzysMpDKVmjZrRoXGHXQbXzoKucUZjhZeISB1TMMahU7NO/Gr4r8oDrVL4lT5mpOnPKSKS6vRf8ji0yGzBKfudEnUZIiJSB3ScTkREJIaCUUREJIaCUUREJIaCUUREJIaCUUREJIaCUUREJIaCUUREJIaCUUREJIaCUUREJIaCUUREJEaDuLuGma0DvghftgQ21XBXu7ttvP3j6Vddn12tbwd8E0cdyWZP/r+K+v30PUstdfldq+/fs3j7Rv1d6+7u7atc4+4NagEeratt4+0fT7/q+uxqPcH9LyP/29fl/1dRv5++Z6m11OV3rb5/z+Ltm8zftYZ4KPXFOtw23v7x9Kuuz558rmRV15+pNt9P37PUUpefq75/z+Ltm7TftQZxKLWhM7Nc38kNOUVqi75nUlcS/V1riCPGhujRqAuQBkHfM6krCf2uacQoIiISQyNGERGRGApGERGRGApGERGRGArGBsjMmprZE2b2RzM7I+p6pH4ys33MbIqZPRd1LVJ/mdmJ4X/LnjGzY2pjnwrGesLM/mxma81sUaX2sWb2sZktM7Prw+aTgefc/QLg+DovVlLW7nzP3H25u58fTaWSynbze/aP8L9lFwM/qo33VzDWH48DY2MbzCwdmAyMA/oBE8ysH9AF+DLsVlyHNUrqe5z4v2ciNfU4u/89+2W4fo8pGOsJd38H2FCpeRiwLPyXewEwFTgBWEUQjqDvgOyG3fyeidTI7nzPLHAnMMPdP6iN99d/FOu3zpSPDCEIxM7A34EfmtlD1N9LfEndqfJ7ZmZtzexhYIiZ3RBNaVKP7Oy/Zz8DjgLGm9nFtfFGGbWxE0kt7r4VODfqOqR+c/f1BOd9RBLG3e8H7q/NfWrEWL+tBrrGvO4StonUJn3PpC7U2fdMwVi/vQ/0NrOeZpYJnAZMi7gmqX/0PZO6UGffMwVjPWFmfwNmA/ub2SozO9/di4DLgJnAUuBZd18cZZ2S2vQ9k7oQ9fdMFxEXERGJoRGjiIhIDAWjiIhIDAWjiIhIDAWjiIhIDAWjiIhIDAWjiIhIDAWjSJIzsy0J2OcKM2sXxXuLJDsFo4iISAwFo0gKMrPjzOw9M/vQzF4zs73C9lvN7Akzm2VmX5jZyWZ2l5ktNLOXzaxRzG6uDdvnmlmvcPueZjY7bL895v2amdnrZvZBuE63lZJ6S8EokpreBQ529yEE96W7NmbdvsARwPHAX4A33X0gsA34fky/TWH7g8B9YdvvgYfC9jUxffOBk9z9AGA08Dszs9r/WCLRUzCKpKYuwEwzWwhcA/SPWTfD3QuBhUA68HLYvhDoEdPvbzGPw8Pnh8a0PxXT14DfmNkC4DWC++DtVSufRCTJKBhFUtMDwIPhyO4iIDtm3XYAdy8BCr38gsglVLwHq8fxvNQZQHvgQHcfDPyv0nuK1BsKRpHU1JLye9GdXcN9/CjmcXb4/N8Et/OBIAxj32+tuxea2Wigew3fUyTpZVTfRUQi1sTMVsW8vge4Ffg/M/sWeAPoWYP9tg4PjW4HJoRtVwBPm9l1wD9j+v4VeDE8dJsL/LcG7yeSEnTbKRERkRg6lCoiIhJDwSgiIhJDwSgiIhJDwSgiIhJDwSgiIhJDwSgiIhJDwSgiIhJDwSgiIhLj/wNZac/m9VvbjwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kqe-SqlbdGWW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "abe4d5e1-50e9-44ef-f911-03bf2c79c54a"
      },
      "source": [
        "#The most important feature is the that doesnt goes zero last, hence from curve it is clear that it is radi\n",
        "print(\"Thus, it is clear from the curve and the co-efficient values that the most important feature is:\",most_imp)"
      ],
      "execution_count": 395,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Thus, it is clear from the curve and the co-efficient values that the most important feature is: TV\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dWkBGyTBYjT-",
        "colab_type": "text"
      },
      "source": [
        "Alternate ways of plotting Co-eff v/s Lambda"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZX2L7xmB6vkj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "outputId": "2e376166-5466-4f8a-cd64-e4676209204d"
      },
      "source": [
        "#Alternative methods of Plotting co-efficients from Ridge Regression\n",
        "#Allocating necessary variables\n",
        "tv=[]\n",
        "radio=[]\n",
        "newspaper=[]\n",
        "for i in range(6):\n",
        "  tv.append(coefs[i][0])\n",
        "  radio.append(coefs[i][1])\n",
        "  newspaper.append(coefs[i][2])\n",
        "#Plotting these variables against lambda\n",
        "lmbda=['0.5', '1', '5', '10', '50', '100']\n",
        "#Forming a dataframe before plotting\n",
        "data_frame = pd.DataFrame({'TV': tv,\n",
        "                    'Newspaper': newspaper,       \n",
        "                    'Radio':radio}, index=lmbda)\n",
        "#Thus plotting them\n",
        "data_frame.plot.bar(stacked=True,figsize=(8,5)) \n",
        "plt.xlabel('Lambda')\n",
        "plt.ylabel('Coefficients')"
      ],
      "execution_count": 396,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Coefficients')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 396
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "orNvkCDP6vrC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "6d69d248-3974-446f-ceb8-dccabe32877e"
      },
      "source": [
        "#Plotting each feature against lambda\n",
        "plt.figure(figsize=(7,7))\n",
        "\n",
        "#plotting TV feature v/s lambda\n",
        "plt.subplot(3, 1, 1) \n",
        "plt.plot(lmbda,tv,'ro-')\n",
        "plt.xlabel('Lambda',fontsize=10)\n",
        "plt.ylabel('TV',fontsize=10)\n",
        "plt.grid(True)\n",
        "\n",
        "#Plotting Radio feature v/s Lambda\n",
        "plt.subplot(3, 1, 2)\n",
        "plt.plot(lmbda,radio,'bo-')\n",
        "plt.xlabel('Lambda',fontsize=10)\n",
        "plt.ylabel('Radio',fontsize=10)\n",
        "plt.grid(True)\n",
        "\n",
        "#Plotting Newspaper feature v/s Lambda\n",
        "plt.subplot(3, 1, 3)\n",
        "plt.plot(lmbda,newspaper,'go-')\n",
        "plt.xlabel('Lambda',fontsize=10)\n",
        "plt.ylabel('Newspaper',fontsize=10)\n",
        "plt.grid(True)\n",
        "\n",
        "plt.tight_layout()"
      ],
      "execution_count": 397,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ImIV1Dd2jlTg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pX9Exr0ujllQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pwYeVpxrjlGR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}